3 citations · 3 across the 5 of their papers we have counts for
5 papers
AtlasMorph: Learning conditional deformable templates for brain MRI
Marianne Rakic, Andrew Hoopes, S. Mazdak Abulnaga +3
Deformable templates, or atlases, are images that represent a prototypical anatomy for a population, and are often enhanced with probabilistic anatomical label maps. They are commo…
Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series
Zeen Chi, Zhongxiao Cong, Clinton J. Wang +6
We present a method for fast biomedical image atlas construction using neural fields. Atlases are key to biomedical image analysis tasks, yet conventional and deep network estimati…
Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series
Yingcheng Liu, Neerav Karani, Neel Dey +5
The placenta plays a crucial role in fetal development. Automated 3D placenta segmentation from fetal EPI MRI holds promise for advancing prenatal care. This paper proposes an effe…
AnyStar: Domain randomized universal star-convex 3D instance segmentation
Neel Dey, S. Mazdak Abulnaga, Benjamin Billot +4
Star-convex shapes arise across bio-microscopy and radiology in the form of nuclei, nodules, metastases, and other units. Existing instance segmentation networks for such structure…
Automatic Segmentation of the Placenta in BOLD MRI Time Series
S. Mazdak Abulnaga, Sean I. Young, Katherine Hobgood +5
Blood oxygen level dependent (BOLD) MRI with maternal hyperoxia can assess oxygen transport within the placenta and has emerged as a promising tool to study placental function. Mea…